MODELING THE DECAY IN AN HBIM STARTING FROM 3D POINT CLOUDS. A FOLLOWED APPROACH FOR CULTURAL HERITAGE KNOWLEDGE

The recent trends in architectural data management imply the scientific and professional collaborations of several disciplines involved in the design, restoration and maintenance. It seems an achieved concept that, in the next future, all the information connected to new interventions or conservation activities on historical buildings will be managed by using a BIM platform. Nowadays the actual range or image based metric survey techniques (mainly produced by using Terrestrial Laser Scanner or photogrammetric platform today more based on projective geometry) allow to generate 3D point clouds, 3D models, orthophotos and other outputs with assessed accuracy. The subsequent conversion of 3D information into parametric components, especially in an historical environment, is not easy and has a lot of open issues. According to the actual BIM commercial software and to the embedded tools or plugin, the paper deals with the methodology followed for the realization of two parametric 3D models (Palazzo Sarmatoris and Smistamento RoundHouse, two historical building in the north-west part of Italy). The paper describes the proposed workflow according to the employed plug-in for automatic reconstruction and to the solution adopted for the well-known problems connected to the modeling phase such as the vaults realization or the 3D irregular surfaces modeling. Finally, the studied strategy for mapping the decay in a BIM environment and the connected results with the conclusions and future perspectives are critically discussed

Metric contrast of thermal 3D models of large industrial facilities obtained by means of low-cost infrared sensors in UAV platforms

Monitoring for maintenance or studies of energy efficiency in buildings, large infrastructure, industrial facilities, etc., are common nowadays. These kind of studies are developed with inspections which determine the state of the facilities that are analysed. The difficulty is increased along with the size and complexity of the facility itself, and even more when the attribute to be surveyed is not noticeable or responsive for the human eye. In recent years, a series of techniques that rely on different sensors mounted on UAVs allow detecting problems that are associated with facilities of large dimensions. Almost all of them work in the visible band (RGB), but the generation of thermal 3D models permits detecting any heat anomaly related to the functioning of these facilities. This research proposes a methodology and workflow for the generation and Metric Contrast of Thermal Models (MCTM). This methodology is metrically applied to a mining-industrial facility in which thermal conditions have great influence for a proper functioning. For this metric contrast, several distances have been measured in the field and compared to those obtained from the models. The average difference between the true magnitude and those obtained from the RGB and thermal models are 5 and 31 cm, and their standard deviations are 7 and 29 cm, respectively. The comparison between the RGB and the thermal model provides an average distance between points is 0.19 m, and for 75% of the points the distance is lesser than 0.35 m. Although the RGB model is more accurate, the precision of the thermal model is enough for the objectives se

MODELING THE DECAY IN AN HBIM STARTING FROM 3D POINT CLOUDS. A FOLLOWED APPROACH FOR CULTURAL HERITAGE KNOWLEDGE

The recent trends in architectural data management imply the scientific and professional collaborations of several disciplines involved in the design, restoration and maintenance. It seems an achieved concept that, in the next future, all the information connected to new interventions or conservation activities on historical buildings will be managed by using a BIM platform. Nowadays the actual range or image based metric survey techniques (mainly produced by using Terrestrial Laser Scanner or photogrammetric platform today more based on projective geometry) allow to generate 3D point clouds, 3D models, orthophotos and other outputs with assessed accuracy. The subsequent conversion of 3D information into parametric components, especially in an historical environment, is not easy and has a lot of open issues. According to the actual BIM commercial software and to the embedded tools or plugin, the paper deals with the methodology followed for the realization of two parametric 3D models (Palazzo Sarmatoris and Smistamento RoundHouse, two historical building in the north-west part of Italy). The paper describes the proposed workflow according to the employed plug-in for automatic reconstruction and to the solution adopted for the well-known problems connected to the modeling phase such as the vaults realization or the 3D irregular surfaces modeling. Finally, the studied strategy for mapping the decay in a BIM environment and the connected results with the conclusions and future perspectives are critically discussed

Efficient UAV flight planning for LOD2 city model improvement

Geometric errors in LoD2 building models can be caused by the modeling algorithm but are often related to the quality of input data. One approach to tackling the modeling errors caused by the quality of input data is to collect additional data with a UAV and remodel the buildings. However, no flight planning approach exists specifically designed for efficient data recollection for model improvement. In this paper, we propose an innovative flight planning approach for this purpose. Contrary to the conventional method that recollects the data covering the entire building roof, our approach only collects the data over the erroneous region and uses it to improve the erroneous model part later. Our algorithm utilizes the existing LiDAR survey data to automatically detect model errors and design the camera networks by considering the roof geometry. We optimize the trajectory that connects the viewpoints with a genetic algorithm and develops an obstacle avoidance function with ray-casting to ensure a collision-free path. The proposed flight plan is implemented in a real-world scene. Our result shows an improved point cloud created through dense image matching with the collected UAV image data. The generated point cloud is successfully used for creating partial building models for improving the original models

An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces

Integrated HBIM-GIS Models for Multi-Scale Seismic Vulnerability Assessment of Historical Buildings

The complexity of historical urban centres progressively needs a strategic improvement in methods and the scale of knowledge concerning the vulnerability aspect of seismic risk. A geographical multi-scale point of view is increasingly preferred in the scientific literature and in Italian regulation policies, that considers systemic behaviors of damage and vulnerability assessment from an urban perspective according to the scale of the data, rather than single building damage analysis. In this sense, a geospatial data sciences approach can contribute towards generating, integrating, and making virtuous relations between urban databases and emergency-related data, in order to constitute a multi-scale 3D database supporting strategies for conservation and risk assessment scenarios. The proposed approach developed a vulnerability-oriented GIS/HBIM integration in an urban 3D geodatabase, based on multi-scale data derived from urban cartography and emergency mapping 3D data. Integrated geometric and semantic information related to historical masonry buildings (specifically the churches) and structural data about architectural elements and damage were integrated in the approach. This contribution aimed to answer the research question supporting levels of knowledge required by directives and vulnerability assessment studies, both about the generative workflow phase, the role of HBIM models in GIS environments and toward user-oriented webGIS solutions for sharing and public use fruition, exploiting the database for expert operators involved in heritage preservation

Adaptive Wavelet Neural Network for Terrestrial Laser Scanner-Based Crack Detection

Objective, accurate, and fast assessment of civil infrastructure conditions is critical to timely assess safety risks. Current practices rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field, which are labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising for automatically identifying structural condition indicators, as they are capable of providing coverage for large areas with accuracy at long ranges. Major challenges in using this technology are in storing significant amount of data and extracting appropriate features enabling condition assessment. This paper proposes a novel adaptive wavelet neural network (WNN)-based approach to compress data into a combination of low- and high-resolution surfaces, and automatically detect concrete cracks and other forms of damage. The adaptive WNN is designed to sequentially self-organize and self-adapt in order to construct an optimized representation. The architecture of the WNN is based on a single-layer neural network consisting of Mexican hat wavelet functions. The strategy is to first construct a low-resolution representation of the point cloud, then detect and localize anomalies, and finally construct a high-resolution representation around these anomalies to enhance their characterization. The approach was verified on four cracked concrete specimens. The experimental results show that the proposed approach was capable of fitting the point cloud, and of detecting and fitting the crack. The results demonstrated data compression of 99.4%, 72.2%, 92.4% and 78.9% for the four specimens when using low resolution fit for crack detection. For specimens 1, 2 and 3, 97.1%, 42.5% and 63.9% compression of data were obtained for crack localization, which is a significant improvement over previous TLS based crack detection and measurement approaches. Using the proposed method for crack detection would enable automatic and remote assessment of structural conditions. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations

Optimization of Photogrammetric Flights with UAVs for the Metric Virtualization of Archaeological Sites. Application to Juliobriga (Cantabria, Spain)

ABSTRACT: Three-dimensional models are required to virtualize heritage sites. In recent years, different techniques that ease their generation have been consolidated, such as photogrammetry with Unmanned Aerial Vehicles (UAVs). Nonmetric cameras allow relatively inexpensive data collections. Traditional aerial photogrammetry has established methodologies, but there are not commonly used recommendations for the selection of parameters when working with UAV platforms. This research applies the Taguchi Design of Experiments Method, with four parameters (height of flight, forward and lateral overlaps, and inclination angle of the sensor) and three levels (L9 matrix and nine flights), to determine the set that offers the best metric goodness and, therefore, the most faithful model. The Roman civitas of Juliobriga (Cantabria, North of Spain) was selected for this experiment. The optimal flight results of the average signal-to-noise ratio analysis were height of 15 m, forward and lateral overlaps of 80%, and inclination of 0° (nadiral). This research also highlights the noticeable contribution of the inclination in the accuracy of the model with respect to the others, which is 16.4 times higher than that of the less relevant one (height of flight). This leads to propose avoiding inclination angle as a variable, and the sole development of nadiral flights to obtain accurate models

Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases

The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided training 2D images. Despite a growing interest in NeRFs, their potential applications in various industrial subdomains are still unexplored. In this paper, we deliver a comprehensive examination of NeRF industrial applications while also providing direction for future research endeavors. We also present a series of proof-of-concept experiments that demonstrate the potential of NeRFs in the industrial domain. These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance. In the video compression experiment, our results show compression savings up to 48\% and 74\% for resolutions of 1920x1080 and 300x168, respectively. The motion estimation experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF) and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with the value of 23 dB and an structural similarity index measure (SSIM) 0.97